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Open AccessJournal ArticleDOI

Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation

David R. Legates, +1 more
- 01 Jan 1999 - 
- Vol. 35, Iss: 1, pp 233-241
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TLDR
In this paper, the goodness-of-fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation-based measures are discussed.
Abstract
Correlation and correlation-based measures (e.g., the coefficient of determination) have been widely used to evaluate the “goodness-of-fit” of hydrologic and hydroclimatic models. These measures are oversensitive to extreme values (outliers) and are insensitive to additive and proportional differences between model predictions and observations. Because of these limitations, correlation-based measures can indicate that a model is a good predictor, even when it is not. In this paper, useful alternative goodness-of-fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation-based measures are discussed. Modifications to these statistics to aid in interpretation are presented. It is concluded that correlation and correlation-based measures should not be used to assess the goodness-of-fit of a hydrologic or hydroclimatic model and that additional evaluation measures (such as summary statistics and absolute error measures) should supplement model evaluation tools.

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Citations
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References
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Journal ArticleDOI

River flow forecasting through conceptual models part I — A discussion of principles☆

TL;DR: In this article, the principles governing the application of the conceptual model technique to river flow forecasting are discussed and the necessity for a systematic approach to the development and testing of the model is explained and some preliminary ideas suggested.
Journal ArticleDOI

On the validation of models

TL;DR: In this paper, it is suggested that the correlation coefficieness between observed and simulated variates is not as good as observed variates, and that correlation can be improved.
Journal ArticleDOI

A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation

TL;DR: This paper reviewed the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule, at a relaxed mathematical level, omitting most proofs, regularity conditions and technical details.
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